Multiple regression is one of the most popular methods used in
statistics as well as in machine learning. We use linear models as a
working model for its simplicity and interpretability. It is important
that we use domain knowledge as much as we could to determine the form
of the response as well as the function format for the factors. Then,
when we have many possible features to be included in the working model
it is inevitable that we need to choose a best possible model with a
sensible criterion. Cp, BIC and
regularizations such as LASSO are introduced. Be aware that if a model
selection is done formally or informally, the inferences obtained with
the final lm() fit may not be valid. Some adjustment will
be needed. This last step is beyond the scope of this class. Check the
current research line that Linda and collaborators are working on.
This homework consists of two parts: the first one is an exercise
(you will feel it being a toy example after the covid case study) to get
familiar with model selection skills such as, Cp and
BIC. The main job is a rather involved case study about
devastating covid19 pandemic. Please read through the case study first.
This project is for sure a great one listed in your CV.
For covid case study, the major time and effort would be needed in EDA portion.
Model building process
Methods
Understand the criteria
CpBICK fold Cross ValidationLASSOPackages
lm(), Anovaregsubsets()glmnet() & cv.glmnet()Review the code and concepts covered during lectures: multiple regression, model selection and penalized regression through elastic net.
If you haven’t done this as part of the homework 2, please attach it here.
ISLR::Auto dataThis will be the last part of the Auto data from ISLR. The original
data contains 408 observations about cars. It has some similarity as the
Cars data that we use in our lectures. To get the data, first install
the package ISLR. The data set Auto should be
loaded automatically. We use this case to go through methods learned so
far.
Final modelling question: We want to explore the effects of each feature as best as possible.
| Var1 | Freq |
|---|---|
| Min. | 9.0 |
| 1st Qu. | 17.0 |
| Median | 22.8 |
| Mean | 23.4 |
| 3rd Qu. | 29.0 |
| Max. | 46.6 |
| Var1 | Freq |
|---|---|
| Min. | 3.00 |
| 1st Qu. | 4.00 |
| Median | 4.00 |
| Mean | 5.47 |
| 3rd Qu. | 8.00 |
| Max. | 8.00 |
| Var1 | Freq |
|---|---|
| Min. | 68 |
| 1st Qu. | 105 |
| Median | 151 |
| Mean | 194 |
| 3rd Qu. | 276 |
| Max. | 455 |
| Var1 | Freq |
|---|---|
| Min. | 46.0 |
| 1st Qu. | 75.0 |
| Median | 93.5 |
| Mean | 104.5 |
| 3rd Qu. | 126.0 |
| Max. | 230.0 |
| Var1 | Freq |
|---|---|
| Min. | 1613 |
| 1st Qu. | 2225 |
| Median | 2804 |
| Mean | 2978 |
| 3rd Qu. | 3615 |
| Max. | 5140 |
| Var1 | Freq |
|---|---|
| Min. | 8.0 |
| 1st Qu. | 13.8 |
| Median | 15.5 |
| Mean | 15.5 |
| 3rd Qu. | 17.0 |
| Max. | 24.8 |
year summary
total 13 years
from 1970-1982
## [1] 3
## 1 2 3
## 245 68 79
## [1] 3
## 1 2 3
## 245 68 79
Origin of car
American: 245
European: 68
Japanese: 79
## [1] 301
Auto names
unique auto names: 301
time have on MPG?mpg
vs. year and report R’s summary output. Is
year a significant variable at the .05 level? State what
effect year has on mpg, if any, according to
this model.| Dependent variable: | |
| mpg | |
| year | 1.230*** |
| (1.060, 1.400) | |
| Constant | -70.000*** |
| (-83.000, -57.000) | |
| Observations | 392 |
| R2 | 0.337 |
| Adjusted R2 | 0.335 |
| Residual Std. Error | 6.360 (df = 390) |
| F Statistic | 198.000*** (df = 1; 390) |
| Note: | p<0.1; p<0.05; p<0.01 |
Year is significant at the 0.01 level. Our model is saying that for every year that goes by, there is about a 1.230 increase in the mpg of a car.
horsepower on top of the variable year
to your linear model. Is year still a significant variable
at the .05 level? Give a precise interpretation of the
year’s effect found here. (Table 4)_| Dependent variable: | |
| mpg | |
| year | 0.657*** |
| (0.527, 0.787) | |
| horsepower | -0.132*** |
| (-0.144, -0.119) | |
| Constant | -12.700** |
| (-23.200, -2.250) | |
| Observations | 392 |
| R2 | 0.685 |
| Adjusted R2 | 0.684 |
| Residual Std. Error | 4.390 (df = 389) |
| F Statistic | 424.000*** (df = 2; 389) |
| Note: | p<0.1; p<0.05; p<0.01 |
Year is significant at the 0.01 level. Our model is saying that for every year that passes by, there is about a .657 increase in the mpg of a car. This effect size decreases from the previous one since we added horsepower to the dataset. (Table 5)
The confidence intervals got a lot smaller going from (i) to
(ii). Since we added more information to the model
(horspower) this reduces some of the variability that we
see when we examine year alone. This reduction in conifidence interval
means that we are likely getting more precise.
lm(mpg ~ year * horsepower). Is the interaction effect
significant at .05 level? Explain the year effect (if any).| Dependent variable: | |
| mpg | |
| year | 2.190*** |
| (1.880, 2.510) | |
| horsepower | 1.050*** |
| (0.820, 1.270) | |
| year:horsepower | -0.016*** |
| (-0.019, -0.013) | |
| Constant | -127.000*** |
| (-150.000, -103.000) | |
| Observations | 392 |
| R2 | 0.752 |
| Adjusted R2 | 0.750 |
| Residual Std. Error | 3.900 (df = 388) |
| F Statistic | 393.000*** (df = 3; 388) |
| Note: | p<0.1; p<0.05; p<0.01 |
All of the variables are significant at the 0.01 level. Year is an extremely significant variable. Our model is saying that for every year that passes by, there is about a 2.190 increase in the mpg of a car. This effect size increases dramatically from the previous models. (Table 6)
Remember that the same variable can play different roles! Take a
quick look at the variable cylinders, and try to use this
variable in the following analyses wisely. We all agree that a larger
number of cylinders will lower mpg. However, we can interpret
cylinders as either a continuous (numeric) variable or a
categorical variable.
cylinders as a
continuous/numeric variable. Is cylinders significant at
the 0.01 level? What effect does cylinders play in this
model?| Dependent variable: | |
| mpg | |
| cylinders | -3.560*** |
| (-3.840, -3.270) | |
| Constant | 42.900*** |
| (41.300, 44.600) | |
| Observations | 392 |
| R2 | 0.605 |
| Adjusted R2 | 0.604 |
| Residual Std. Error | 4.910 (df = 390) |
| F Statistic | 597.000*** (df = 1; 390) |
| Note: | p<0.1; p<0.05; p<0.01 |
Cylinders is significant at the 0.01 level. Our model is saying that for every 1 cylinder added, there is about a 3.560 increase in the mpg of a car. (Table 7)
cylinders as a
categorical/factor. Is cylinders significant at the .01
level? What is the effect of cylinders in this model?
Describe the cylinders effect over mpg.| Dependent variable: | |
| mpg | |
| factor(cylinders)4 | 8.730*** |
| (4.080, 13.400) | |
| factor(cylinders)5 | 6.820* |
| (-0.217, 13.900) | |
| factor(cylinders)6 | -0.577 |
| (-5.290, 4.140) | |
| factor(cylinders)8 | -5.590** |
| (-10.300, -0.894) | |
| Constant | 20.600*** |
| (15.900, 25.200) | |
| Observations | 392 |
| R2 | 0.641 |
| Adjusted R2 | 0.638 |
| Residual Std. Error | 4.700 (df = 387) |
| F Statistic | 173.000*** (df = 4; 387) |
| Note: | p<0.1; p<0.05; p<0.01 |
Only 4 Cylinders is significant at the 0.01 level. Our model is saying that for every 1 cylinder added, there is about a 3.560 increase in the mpg of a car. (Table 7)
cylinders as a continuous and categorical variable in your
models?From a practical sense it’s not feasible to consider cylinders as a continuous variable because it’ll out put results that don’t make sense. It will assume that the more cylinders you have, the lower your mpg will be. However, considering cylinders as a categorical variable allows you to see that having different numbers of cylinders is not a linear relationship.
mpg is linear
in cylinders vs. fit1: mpg relates to
cylinders as a categorical variable at .01 level?Yes you can using anova(H_0, H_1). There is strong evidence of
rejecting the null hypothesis that fit0: mpg is linear in
cylinders vs. fit1: mpg relates to
cylinders as a categorical variable
## Analysis of Variance Table
##
## Model 1: mpg ~ cylinders
## Model 2: mpg ~ factor(cylinders)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 390 9416
## 2 387 8544 3 871 13.2 3.4e-08 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
GPM=1/MPG. Compare residual plots of MPG or GPM as
responses and see which one might yield a more satisfactory
patterns.In addition, can you provide some background knowledge to support the
notion: it makes more sense to model GPM?
You may also explore by adding interactions and higher order terms. The model(s) should be as parsimonious (simple) as possible, unless the gain in accuracy is significant from your point of view.
Use Mallow’s \(C_p\) or BIC to select the model.
mpg of a car that is: built in 1983, in the
US, red, 180 inches long, 8 cylinders, 350 displacement, 260 as
horsepower, and weighs 4,000 pounds. Give a 95% CI.The outbreak of the novel Corona virus disease 2019 (COVID-19) was declared a public health emergency of international concern by the World Health Organization (WHO) on January 30, 2020. Upwards of 755 million cases have been confirmed worldwide, with nearly 6.8 million associated deaths by Feb of 2023. Within the US alone, there have been over 1.1 million deaths and upwards of 102 million cases reported by Feb of 2023. Governments around the world have implemented and suggested a number of policies to lessen the spread of the pandemic, including mask-wearing requirements, travel restrictions, business and school closures, and even stay-at-home orders. The global pandemic has impacted the lives of individuals in countless ways, and though many countries have begun vaccinating individuals, the long-term impact of the virus remains unclear.
The impact of COVID-19 on a given segment of the population appears to vary drastically based on the socioeconomic characteristics of the segment. In particular, differing rates of infection and fatalities have been reported among different racial groups, age groups, and socioeconomic groups. One of the most important metrics for determining the impact of the pandemic is the death rate, which is the proportion of people within the total population that die due to the the disease.
We assemble this dataset for our research with the goal to investigate the effectiveness of lockdown on flattening the COVID curve. We provide a portion of the cleaned dataset for this case study.
There are two main goals for this case study.
Remark1: The data and the statistics reported here were collected before February of 2021.
Remark 2: A group of RAs spent tremendous amount of time working together to assemble the data. It requires data wrangling skills.
Remark 3: Please keep track with the most updated version of this write-up.
The data comes from several different sources:
In this case study, we use the following three nearly cleaned data:
Among all data, the unique identifier of county is
FIPS.
The cleaning procedure is attached in
Appendix 2: Data cleaning You may go through it if you are
interested or would like to make any changes.
It may need more data wrangling.
First read in the data.
# county-level socialeconomic information
county_data <- fread("data/covid_county.csv")
# county-level COVID case and death
covid_rate <- fread("data/covid_rates.csv")
# county-level lockdown dates
# covid_intervention <- fread("data/covid_intervention.csv")The detailed description of variables is in
Appendix 1: Data description. Please get familiar with the
variables. Summarize the two data briefly.
Race Distribution across US counties
## Warning: Using an external vector in selections was deprecated in tidyselect 1.1.0.
## ℹ Please use `all_of()` or `any_of()` instead.
## # Was:
## data %>% select(cols_selected)
##
## # Now:
## data %>% select(all_of(cols_selected))
##
## See <https://tidyselect.r-lib.org/reference/faq-external-vector.html>.
Age Distribution across US counties
Education Distribution across US counties
Employment Distribution across US counties
## $`1`
##
## $`2`
##
## attr(,"class")
## [1] "list" "ggarrange"
Income Distribution across US counties
## Warning: Using an external vector in selections was deprecated in tidyselect 1.1.0.
## ℹ Please use `all_of()` or `any_of()` instead.
## # Was:
## data %>% select(income_cols)
##
## # Now:
## data %>% select(all_of(income_cols))
##
## See <https://tidyselect.r-lib.org/reference/faq-external-vector.html>.
Unemployment Distribution across US counties
## Warning: Using an external vector in selections was deprecated in tidyselect 1.1.0.
## ℹ Please use `all_of()` or `any_of()` instead.
## # Was:
## data %>% select(empl_cols)
##
## # Now:
## data %>% select(all_of(empl_cols))
##
## See <https://tidyselect.r-lib.org/reference/faq-external-vector.html>.
COVID-19 Total Cases and Deaths by state and county
It is crucial to decide the right granularity for visualization and analysis. We will compare daily vs weekly total new cases by state and we will see it is hard to interpret daily report.
## Warning: The `add` argument of `group_by()` is deprecated as of dplyr 1.0.0.
## ℹ Please use the `.add` argument instead.
## ℹ The deprecated feature was likely used in the dplyr package.
## Please report the issue at <]8;;https://github.com/tidyverse/dplyr/issueshttps://github.com/tidyverse/dplyr/issues]8;;>.
weekly_case_per100k. Plot the spaghetti plots of
weekly_case_per100k by state. Use
TotalPopEst2019 as population.## `summarise()` has grouped output by 'State'. You can override using the
## `.groups` argument.
lockdown/when the first cases were reported, vaccine access, and governmental attitudes towards COVID protocol
covid_intervention to see whether the
effectiveness of lockdown in flattening the curve.limits argument in scale_fill_gradient() or
use facet_wrap(); use lubridate::month() and
lubridate::year() to extract month and year from date; use
tidyr::complete(state, month, fill = list(new_case_per100k = NA))
to complete the missing months with no cases.)## `summarise()` has grouped output by 'State'. You can override using the
## `.groups` argument.
plotly to animate the monthly maps in
i). Does it reveal any systematic way to capture the dynamic changes
among states? (Hints: Follow Appendix 3: Plotly heatmap:: in
Module 6 regularization lecture to plot the heatmap using
plotly. Use frame argument in
add_trace() for animation. plotly only
recognizes abbreviation of state names. Use
unique(us_map(regions = "states") %>% select(abbr, full))
to get the abbreviation and merge with the data to get state
abbreviation.)We now try to build a good parsimonious model to find possible factors related to death rate on county level. Let us not take time series into account for the moment and use the total number as of Feb 1, 2021.
Create the response variable total_death_per100k as
the total of number of COVID deaths per 100k by Feb 1, 2021. We
suggest to take log transformation as
log_total_death_per100k = log(total_death_per100k + 1).
Merge total_death_per100k to county_data for
the following analysis.
Select possible variables in county_data as
covariates. We provide county_data_sub, a subset variables
from county_data, for you to get started. Please add any
potential variables as you wish.
Report missing values in your final subset of variables.
In the following anaylsis, you may ignore the missing values.
county_data_sub <- county_data %>%
select(County, State, FIPS, Deep_Pov_All, PovertyAllAgesPct, PerCapitaInc, UnempRate2019, PctEmpFIRE, PctEmpConstruction, PctEmpTrans, PctEmpMining, PctEmpTrade, PctEmpInformation, PctEmpAgriculture, PctEmpManufacturing, PctEmpServices, PopDensity2010, OwnHomePct, Age65AndOlderPct2010, TotalPop25Plus, Under18Pct2010, Ed2HSDiplomaOnlyPct, Ed3SomeCollegePct, Ed4AssocDegreePct, Ed5CollegePlusPct, ForeignBornPct, Net_International_Migration_Rate_2010_2019, NetMigrationRate1019, NaturalChangeRate1019, TotalPopEst2019, WhiteNonHispanicPct2010, NativeAmericanNonHispanicPct2010, BlackNonHispanicPct2010, AsianNonHispanicPct2010, HispanicPct2010, Type_2015_Update, RuralUrbanContinuumCode2013, UrbanInfluenceCode2013, Perpov_1980_0711, HiCreativeClass2000, HiAmenity, Retirement_Destination_2015_Update)Use LASSO to choose a parsimonious model with all available
sensible county-level information. Force in State in
the process. Why we need to force in State? You may use
lambda.1se to choose a smaller model.
Use Cp or BIC to fine tune the LASSO model from
iii). Again force in State in the process. (You could
do backward elimination to avoid using Cp or BIC)
If necessary, reduce the model from iv) to a final model with all variables being significant at 0.05 level. Are the linear model assumptions all reasonably met?
It has been shown that COVID affects elderly the most. It is also claimed that the COVID death rate among African Americans and Latinos is higher. Does your analysis support these arguments?
Based on your final model, summarize your findings. In particular, summarize the state effect controlling for others. Provide intervention recommendations to policy makers to reduce COVID death rate.
What else can we do to improve our model? What other important information we may have missed?
(Optional) Would your findings be very different if you had
refined the data in some way or imputed the missing values in part ii).
Check PCA lecture, section 10 for imputations via
softImpute.
Please summarize this project as follows (no more than one page):
Goal of the study
Data
Analyses
Methods used
Findings
Limitations
A detailed summary of the variables in each data set follows:
Income: Poverty level and household income
Jobs: Employment type, rate, and change
UnempRate2007-2019: Unemployment rate, 2007-2019
NumEmployed2007-2019: Employed, 2007-2019
NumUnemployed2007-2019: Unemployed, 2007-2019
PctEmpChange1019: Percent employment change, 2010-19
PctEmpChange1819: Percent employment change, 2018-19
PctEmpChange0719: Percent employment change, 2007-19
PctEmpChange0710: Percent employment change, 2007-10
NumCivEmployed: Civilian employed population 16 years and over,
2014-18
NumCivLaborforce2007-2019: Civilian labor force, 2007-2019
PctEmpFIRE: Percent of the civilian labor force 16 and over
employed in finance and insurance, and real estate and rental and
leasing, 2014-18
PctEmpConstruction: Percent of the civilian labor force 16 and
over employed in construction, 2014-18
PctEmpTrans: Percent of the civilian labor force 16 and over employed in transportation, warehousing and utilities, 2014-18
PctEmpMining: Percent of the civilian labor force 16 and over
employed in mining, quarrying, oil and gas extraction, 2014-18
PctEmpTrade: Percent of the civilian labor force 16 and over
employed in wholesale and retail trade, 2014-18
PctEmpInformation: Percent of the civilian labor force 16 and
over employed in information services, 2014-18
PctEmpAgriculture: Percent of the civilian labor force 16 and
over employed in agriculture, forestry, fishing, and hunting,
2014-18
PctEmpManufacturing: Percent of the civilian labor force 16 and
over employed in manufacturing, 2014-18
PctEmpServices: Percent of the civilian labor force 16 and over
employed in services, 2014-18
PctEmpGovt: Percent of the civilian labor force 16 and over employed in public administration, 2014-18
People: Population size, density, education level, race, age, household size, and migration rates
PopDensity2010: Population density, 2010
LandAreaSQMiles2010: Land area in square miles, 2010
TotalHH: Total number of households, 2014-18
TotalOccHU: Total number of occupied housing units, 2014-18
AvgHHSize: Average household size, 2014-18
OwnHomeNum: Number of owner occupied housing units, 2014-18
OwnHomePct: Percent of owner occupied housing units, 2014-18
NonEnglishHHPct: Percent of non-English speaking households of
total households, 2014-18
HH65PlusAlonePct: Percent of persons 65 or older living alone,
2014-18
FemaleHHPct: Percent of female headed family households of total
households, 2014-18
FemaleHHNum: Number of female headed family households,
2014-18
NonEnglishHHNum: Number of non-English speaking households,
2014-18
HH65PlusAloneNum: Number of persons 65 years or older living alone, 2014-18
Age65AndOlderPct2010: Percent of population 65 or older, 2010
Age65AndOlderNum2010: Population 65 years or older, 2010
TotalPop25Plus: Total population 25 and older, 2014-18 - 5-year
average
Under18Pct2010: Percent of population under age 18, 2010
Under18Num2010: Population under age 18, 2010
Ed1LessThanHSPct: Percent of persons with no high school diploma
or GED, adults 25 and over, 2014-18
Ed2HSDiplomaOnlyPct: Percent of persons with a high school
diploma or GED only, adults 25 and over, 2014-18
Ed3SomeCollegePct: Percent of persons with some college
experience, adults 25 and over, 2014-18
Ed4AssocDegreePct: Percent of persons with an associate’s degree,
adults 25 and over, 2014-18
Ed5CollegePlusPct: Percent of persons with a 4-year college
degree or more, adults 25 and over, 2014-18
Ed1LessThanHSNum: No high school, adults 25 and over, 2014-18
Ed2HSDiplomaOnlyNum: High school only, adults 25 and over,
2014-18
Ed3SomeCollegeNum: Some college experience, adults 25 and over,
2014-18
Ed4AssocDegreeNum: Number of persons with an associate’s degree,
adults 25 and over, 2014-18
Ed5CollegePlusNum: College degree 4-years or more, adults 25 and over, 2014-18
ForeignBornPct: Percent of total population foreign born,
2014-18
ForeignBornEuropePct: Percent of persons born in Europe,
2014-18
ForeignBornMexPct: Percent of persons born in Mexico, 2014-18
ForeignBornCentralSouthAmPct: Percent of persons born in Central
or South America, 2014-18
ForeignBornAsiaPct: Percent of persons born in Asia, 2014-18
ForeignBornCaribPct: Percent of persons born in the Caribbean,
2014-18
ForeignBornAfricaPct: Percent of persons born in Africa,
2014-18
ForeignBornNum: Number of people foreign born, 2014-18
ForeignBornCentralSouthAmNum: Number of persons born in Central
or South America, 2014-18
ForeignBornEuropeNum: Number of persons born in Europe,
2014-18
ForeignBornMexNum: Number of persons born in Mexico, 2014-18
ForeignBornAfricaNum: Number of persons born in Africa,
2014-18
ForeignBornAsiaNum: Number of persons born in Asia, 2014-18
ForeignBornCaribNum: Number of persons born in the Caribbean, 2014-18
Net_International_Migration_Rate_2010_2019: Net international
migration rate, 2010-19
Net_International_Migration_2010_2019: Net international
migration, 2010-19
Net_International_Migration_2000_2010: Net international
migration, 2000-10
Immigration_Rate_2000_2010: Net international migration rate,
2000-10
NetMigrationRate0010: Net migration rate, 2000-10
NetMigrationRate1019: Net migration rate, 2010-19
NetMigrationNum0010: Net migration, 2000-10
NetMigration1019: Net Migration, 2010-19
NaturalChangeRate1019: Natural population change rate,
2010-19
NaturalChangeRate0010: Natural population change rate,
2000-10
NaturalChangeNum0010: Natural change, 2000-10
NaturalChange1019: Natural population change, 2010-19
TotalPop2010: Population size 4/1/2010 Census
TotalPopEst2010: Population size 7/1/2010
TotalPopEst2011: Population size 7/1/2011
TotalPopEst2012: Population size 7/1/2012
TotalPopEst2013: Population size 7/1/2013
TotalPopEst2014: Population size 7/1/2014
TotalPopEst2015: Population size 7/1/2015
TotalPopEst2016: Population size 7/1/2016
TotalPopEst2017: Population size 7/1/2017
TotalPopEst2018: Population size 7/1/2018
TotalPopEst2019: Population size 7/1/2019
TotalPopACS: Total population, 2014-18 - 5-year average
TotalPopEstBase2010: County Population estimate base 4/1/2010
NonHispanicAsianPopChangeRate0010: Population change rate
Non-Hispanic Asian, 2000-10
PopChangeRate1819: Population change rate, 2018-19
PopChangeRate1019: Population change rate, 2010-19
PopChangeRate0010: Population change rate, 2000-10
NonHispanicNativeAmericanPopChangeRate0010: Population change
rate Non-Hispanic Native American, 2000-10
HispanicPopChangeRate0010: Population change rate Hispanic,
2000-10
MultipleRacePopChangeRate0010: Population change rate multiple
race, 2000-10
NonHispanicWhitePopChangeRate0010: Population change rate
Non-Hispanic White, 2000-10
NonHispanicBlackPopChangeRate0010: Population change rate Non-Hispanic African American, 2000-10
MultipleRacePct2010: Percent multiple race, 2010
WhiteNonHispanicPct2010: Percent Non-Hispanic White, 2010
NativeAmericanNonHispanicPct2010: Percent Non-Hispanic Native
American, 2010
BlackNonHispanicPct2010: Percent Non-Hispanic African American,
2010
AsianNonHispanicPct2010: Percent Non-Hispanic Asian, 2010
HispanicPct2010: Percent Hispanic, 2010
MultipleRaceNum2010: Population size multiple race, 2010
WhiteNonHispanicNum2010: Population size Non-Hispanic White,
2010
BlackNonHispanicNum2010: Population size Non-Hispanic African
American, 2010
NativeAmericanNonHispanicNum2010: Population size Non-Hispanic
Native American, 2010
AsianNonHispanicNum2010: Population size Non-Hispanic Asian,
2010
HispanicNum2010: Population size Hispanic, 2010
##County classifications
Type of county (rural or urban on a rural-urban continuum scale)
Type_2015_Recreation_NO: Recreation counties, 2015 edition
Type_2015_Farming_NO: Farming-dependent counties, 2015
edition
Type_2015_Mining_NO: Mining-dependent counties, 2015 edition
Type_2015_Government_NO: Federal/State government-dependent
counties, 2015 edition
Type_2015_Update: County typology economic types, 2015
edition
Type_2015_Manufacturing_NO: Manufacturing-dependent counties,
2015 edition
Type_2015_Nonspecialized_NO: Nonspecialized counties, 2015
edition
RecreationDependent2000: Nonmetro recreation-dependent,
1997-00
ManufacturingDependent2000: Manufacturing-dependent, 1998-00
FarmDependent2003: Farm-dependent, 1998-00
EconomicDependence2000: Economic dependence, 1998-00
RuralUrbanContinuumCode2003: Rural-urban continuum code, 2003
UrbanInfluenceCode2003: Urban influence code, 2003
RuralUrbanContinuumCode2013: Rural-urban continuum code, 2013
UrbanInfluenceCode2013: Urban influence code, 2013
Noncore2013: Nonmetro noncore, outside Micropolitan and
Metropolitan, 2013
Micropolitan2013: Micropolitan, 2013
Nonmetro2013: Nonmetro, 2013
Metro2013: Metro, 2013
Metro_Adjacent2013: Nonmetro, adjacent to metro area, 2013
Noncore2003: Nonmetro noncore, outside Micropolitan and
Metropolitan, 2003
Micropolitan2003: Micropolitan, 2003
Metro2003: Metro, 2003
Nonmetro2003: Nonmetro, 2003
NonmetroNotAdj2003: Nonmetro, nonadjacent to metro area, 2003
NonmetroAdj2003: Nonmetro, adjacent to metro area, 2003
Oil_Gas_Change: Change in the value of onshore oil and natural
gas production, 2000-11
Gas_Change: Change in the value of onshore natural gas
production, 2000-11
Oil_Change: Change in the value of onshore oil production, 2000-11
Hipov: High poverty counties, 2014-18
Perpov_1980_0711: Persistent poverty counties, 2015 edition
PersistentChildPoverty_1980_2011: Persistent child poverty
counties, 2015 edition
PersistentChildPoverty2004: Persistent child poverty counties,
2004
PersistentPoverty2000: Persistent poverty counties, 2004
Low_Education_2015_update: Low education counties, 2015 edition
LowEducation2000: Low education, 2000
HiCreativeClass2000: Creative class, 2000
HiAmenity: High natural amenities
RetirementDestination2000: Retirement destination, 1990-00
Low_Employment_2015_update: Low employment counties, 2015 edition
Population_loss_2015_update: Population loss counties, 2015 edition
Retirement_Destination_2015_Update: Retirement destination counties, 2015 edition
The raw data sets are dirty and need transforming before we can do
our EDA. It takes time and efforts to clean and merge different data
sources so we provide the final output of the cleaned and merged data.
The cleaning procedure is as follows. Please read through to understand
what is in the cleaned data. We set eval = data_cleaned in
the following cleaning chunks so that these cleaning chunks will only
run if any of data/covid_county.csv,
data/covid_rates.csv or
data/covid_intervention.csv does not exist.
# Indicator to check whether the data files exist
data_cleaned <- !(file.exists("data/covid_county.csv") &
file.exists("data/covid_rates.csv") &
file.exists("data/covid_intervention.csv"))We first read in the table using data.table::fread(), as
we did last time.
# COVID case/mortality rate data
covid_rates <- fread("data/us_counties.csv", na.strings = c("NA", "", "."))
nyc <- fread("data/nycdata.csv", na.strings = c("NA", "", "."))
# Socioeconomic data
income <- fread("data/income.csv", na.strings = c("NA", "", "."))
jobs <- fread("data/jobs.csv", na.strings = c("NA", "", "."))
people <- fread("data/people.csv", na.strings = c("NA", "", "."))
county_class <- fread("data/county_classifications.csv", na.strings = c("NA", "", "."))
# Internvention policy data
int_dates <- fread("data/intervention_dates.csv", na.strings = c("NA", "", "."))The original NYC data contains more information than we need. We
extract only the number of cases and deaths and format it the same as
the covid_rates data.
# NYC county fips matching table
nyc_fips <- data.table(FIPS = c('36005', '36047', '36061', '36081', '36085'),
County = c("BX", "BK", "MN", "QN", "SI"))
# nyc case
nyc_case <- nyc[,.(date = as.Date(date_of_interest, "%m/%d/%Y"),
BX = BX_CASE_COUNT,
BK = BK_CASE_COUNT,
MN = MN_CASE_COUNT,
QN = QN_CASE_COUNT,
SI = SI_CASE_COUNT)]
nyc_case %<>%
pivot_longer(cols = BX:SI,
names_to = "County",
values_to = "cases") %>%
arrange(date) %>%
group_by(County) %>%
mutate(cum_cases = cumsum(cases))
# nyc death
nyc_death <- nyc[,.(date = as.Date(date_of_interest, "%m/%d/%Y"),
BX = BX_DEATH_COUNT,
BK = BK_DEATH_COUNT,
MN = MN_DEATH_COUNT,
QN = QN_DEATH_COUNT,
SI = SI_DEATH_COUNT)]
nyc_death %<>%
pivot_longer(cols = BX:SI,
names_to = "County",
values_to = "deaths") %>%
arrange(date) %>%
group_by(County) %>%
mutate(cum_deaths = cumsum(deaths))
nyc_rates <- merge(nyc_case,
nyc_death,
by = c("date", "County"),
all.x= T)
nyc_rates <- merge(nyc_rates,
nyc_fips,
by = "County")
nyc_rates$State <- "New York"
nyc_rates %<>%
select(date, FIPS, County, State, cum_cases, cum_deaths) %>%
arrange(FIPS, date)We only consider cases and death in continental US. Alaska, Hawaii,
and Puerto Rico have 02, 15, and 72 as their respective first 2 digits
of their FIPS. We use the %/% operator for integer division
to get the first 2 digits of FIPS. We also remove Virgin Islands and
Northern Mariana Islands. All data of counties in NYC are aggregated as
County == "New York City" in covid_rates with
no FIPS, so we combine the NYC data into covid_rate.
covid_rates <- covid_rates %>%
arrange(fips, date) %>%
filter(!(fips %/% 1000 %in% c(2, 15, 72))) %>%
filter(county != "New York City") %>%
filter(!(state %in% c("Virgin Islands", "Northern Mariana Islands"))) %>%
rename(FIPS = "fips",
County = "county",
State = "state",
cum_cases = "cases",
cum_deaths = "deaths")
covid_rates$date <- as.Date(covid_rates$date)
covid_rates <- rbind(covid_rates,
nyc_rates)We set the week of Jan 21, 2020 (the first case of COVID case in US) as the first week (2020-01-19 to 2020-01-25).
covid_rates[, week := (interval("2020-01-19", date) %/% weeks(1)) + 1]Merge the TotalPopEst2019 variable from the demographic
data with covid_rates by FIPS.
covid_rates <- merge(covid_rates[!is.na(FIPS)],
people[,.(FIPS = as.character(FIPS),
TotalPopEst2019)],
by = "FIPS",
all.x = TRUE)NA values in the covid_rates data set correspond to a
county not having confirmed cases/deaths. We replace the NA values in
these columns with zeros. FIPS for Kansas city, Missouri, Rhode Island
and some others are missing. We drop them for the moment and output the
data up to week 57 as covid_rates.csv.
covid_rates$cum_cases[is.na(covid_rates$cum_cases)] <- 0
covid_rates$cum_deaths[is.na(covid_rates$cum_deaths)] <- 0fwrite(covid_rates %>%
filter(week < 58) %>%
arrange(FIPS, date),
"data/covid_rates.csv")int_datesWe convert the columns representing dates in int_dates
to R Date types using as.Date(). We will need to specify
that the origin parameter is "0001-01-01". We
output the data as covid_intervention.csv.
int_dates <- int_dates[-1,]
date_cols <- names(int_dates)[-(1:3)]
int_dates[, (date_cols) := lapply(.SD, as.Date, origin = "0001-01-01"),
.SDcols = date_cols]
fwrite(int_dates, "data/covid_intervention.csv")Merge the demographic data sets by FIPS and output as
covid_county.csv.
countydata <-
merge(x = income,
y = merge(
x = people,
y = jobs,
by = c("FIPS", "State", "County")),
by = c("FIPS", "State", "County"),
all = TRUE)
countydata <-
merge(
x = countydata,
y = county_class %>% rename(FIPS = FIPStxt),
by = c("FIPS", "State", "County"),
all = TRUE
)
# Check dimensions
# They are now 3279 x 208
dim(countydata)
fwrite(countydata, "data/covid_county.csv")